Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods

COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical m...

Full description

Saved in:
Bibliographic Details
Published in:JMIR medical informatics 2021-04, Vol.9 (4), p.e25066-e25066
Main Authors: Cummings, Brandon C, Ansari, Sardar, Motyka, Jonathan R, Wang, Guan, Medlin, Jr, Richard P, Kronick, Steven L, Singh, Karandeep, Park, Pauline K, Napolitano, Lena M, Dickson, Robert P, Mathis, Michael R, Sjoding, Michael W, Admon, Andrew J, Blank, Ross, McSparron, Jakob I, Ward, Kevin R, Gillies, Christopher E
Format: Article
Language:English
Subjects:
Blood pressure
Coronaviruses
COVID-19
Electronic health records
Hospitals
Intensive care
Laboratories
Metabolism
Original Paper
Pandemics
Patients
Population
Predictive analytics
Respiratory failure
Validation studies
Ventilators
Vital signs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. In non-COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P
ISSN:2291-9694
2291-9694
DOI:10.2196/25066